An Integrated Analysis of Mobile Application Usage and In-App Advertising Response
Liye Ma and Baohong Sun, 2016, 16-112
Mobile applications account for 86% of consumers’ time spent on mobile devices, and mobile advertising, projected to exceed $100 billion in revenue in 2016, is increasingly shifting to in-app advertising. To successfully engage with consumers, marketers need to understand more about their mobile application usage and in-app advertising response across time and locations.
In this study, Liye Ma and Baohong Sun develop an integrated model of consumers’ mobile app usage and advertising response. Their study is the first to link consumers’ usage of mobile applications with response to in-app advertisements, and the first to evaluate the effects of consumers’ underlying involvement in different activities and the application context on advertising response in a real world setting.
Their dataset, obtained from a large mobile advertising platform company, includes panel data on both application usage and advertising responses across four categories of mobile applications: information, entertainment, utility, and social. Their dataset includes 424 mobile applications and the impressions and clicks of 14 advertising campaigns.
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Overall, their analysis shows that consumers’ usage of different types of mobile applications and their propensity to click in-app advertisements vary significantly over time. Involvement in information activities is more pronounced in the morning, while involvement in utility and entertainment activities peak later in the day, and involvement in entertainment activities persists into evening. Involvement in social activities varies less dramatically over time, with a double-peak in the morning and early afternoon, remaining stable into early evening.
Further, earlier usage of entertainment, information, and utility applications leads to reduced use later on, while earlier use of social applications leads to higher use later.
On advertising response, repeated delivery of a product trial advertisement reduces the probability to click, while repeated delivery of a promotion advertisement increases the click probability.
Their analysis also shows that consumers’ ad clicks are related to not only the specific application in which the ad is displayed, but also to their usage of other applications at around the same time. Higher involvement in entertainment activities significantly reduces a consumer’s interest in advertisements. Higher involvement in utility and information activities also reduces click propensity, although to lesser extents. Higher involvement in social activities, in contrast, increases a consumer’s interest in advertisements.
At the same time, estimates of the contextual effect show that information applications provide the most favorable context for clicking advertisements, while social applications are least conducive to clicks. This contrast between the contextual effect and the effect of underlying involvement underscores the importance of analyzing application usage and in-app advertising response in an integrated framework.
Through simulation, the authors show that targeted ad delivery strategies derived from their model yield significantly higher click-through rates than the benchmark strategy, and the advantage is greater for lower ad impression quotas which require more precise targeting. The targeting strategies deliver more ad impressions in times that are more conducive to clicks, thus better aligning the two than does the benchmark strategy. Ad impressions delivered based on inference of individual consumer level involvements are also shown to be more effective than those based on population level estimates or only time effects, suggesting there is much potential to individual consumer based targeting.
Liye Ma is Assistant Professor of Marketing, Robert H. Smith School of Business, University of Maryland. Baohong Sun is Dean’s Distinguished Chair Professor of Marketing, Cheung Kong Graduate School of Business.
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